(1) Technical Field
The present invention relates to a high-performance general-purpose analogical reasoning system. More specifically, the present invention relates semantic-based hybrid architecture for analogical reasoning using non-temporal activation multiplexing capable of finding correspondences between a novel situation and a known situation using relational symmetries, object similarities, or a combination of the two.
(2) Description of Related Art
Analogical reasoning is a process by which a solution can be inferred based on existing knowledge. Some prior art exists for generic implementations of analogical reasoning methods and devices (U.S. Pat. No. 5,276,774), autonomous case-based reasoning agents (U.S. Pat. No. 5,852,814), relational artificial intelligence (AI) system (U.S. Pat. No. 5,473,732), analogical evaluation and control systems (U.S. Pat. No. 4,905,162, U.S. Pat. No. 4,777,585), as well as specific implementations for protocol execution and specification (U.S. Pat. No. 4,899,290), positional planning (U.S. Pat. No. 5,696,693), and topographical inference (U.S. Pat. No. 5,784,540). For the most part, these patents employ different reasoning strategies (e.g., case-based reasoning) or are applied in specific domains (i.e., planning or topographical inference).
Although there are reasoning models in the literature that mention analogical strategies, each has several shortcomings.
A Structure Mapping Engine (SME) is a program for studying analogical reasoning and generally includes a model for constructing matching algorithms consistent with structure-mapping theory. SME is described in “The Structure-Mapping Engine: Algorithm and Examples,” 1989, Artificial Intelligence, 41, 1-63, by Falkenhainer, B., Forbus, K. D., and Gentner, D. The SME model does not contain semantic information explicitly. Instead, SME employs attributes (which are explicitly predicated facts about an entity, e.g., HEAVY John). SME uses attributes (or function-constant pairings) to perform some mappings, and employs the identicality constraint to perform others. However, the simplifying assumptions that SME makes about semantics may lead to difficulty in certain situations.
Another example is ACT-R. ACT-R is a cognitive architecture containing a theory about human cognition. ACT-R was developed by the ACT-R Research Group at Carnegie Mellon University. Carnegie Mellon University is located at 5000 Forbes Avenue, Pittsburgh, Pa. 15213, U.S.A. One major problem with the path-mapping algorithm of ACT-R is its consideration of only one “path” at a time. That is, a single non-branching sub-graph connecting an entity or relation to a root relation is specified during mapping. This fact may introduce errors that seem unlikely to be committed by humans and are not beneficial to solving difficult problems. In short, when an object or relation serves as the argument to two or more other relations, path-mapping will not consider the joint role of that object or relation, but only a single randomly selected role.
Another example of a cognitive architecture is Prodigy-Analogy. Prodigy-Analogy is a combined case-based and generative planner developed by researchers at Carnegie Mellon University. In the context of more general problem-solving, Prodigy-Analogy may be limited by the need for user preconditions that specify the desired end-state of the problem space in significant detail. This may be a liability when the solution is not well defined. While the specification of the goal state in some domains is trivial (e.g., a route-planning problem cannot be defined without a specific destination), it may prove less obvious in other domains (e.g., the goal “defend town X” is not heavily constrained). In some sense, the derivational analogy process is excellent for planning given a well-defined goal state, but that some other procedure may be necessary for forming that well-defined goal state.
Like SME, the VivoMind Analogy Engine (VAE) relies heavily on syntax and structure, paying little attention to semantics except in terms of labels on graph nodes. The VAE is an analogy finder that uses graphs for its knowledge representation. The VAE was described in an article by John Sowa and Arun Majumdar, entitled, “Analogical reasoning,” co-authored with Arun K. Majumdar, in de Moor, Lex, Ganter, eds., Conceptual Structures for Knowledge Creation and Communication, Proceedings of ICCS 2003, LNAI 2746, Springer-Verlag, Berlin, 2003, pp. 16-36.
Another reasoning system is the Learning and Inference with Schemas and Analogies (LISA) system designed by John Hummel and Keith Holyoak. John Hummel is a professor at the University of Illinois at Urbana-Champaign, located at 603 East Daniel St., Champaign, Ill. 61820. Keith Holyoak is a professor at the University of California, Los Angeles, located at the Neuroscience and Genetics Research Center, 695 Charles Young Drive, Los Angeles, Calif. 90095-1761.
Although functional for simple inferences, LISA is ill-suited as an extensible, performance-focused reasoner for real-world applications. LISA's implementation of temporal binding necessitates dozens of passes though the network to successfully segregate disparate roles and fillers. LISA requires its reasoning algorithm to traverse its neural network hundreds of times. Each successive pass through the network increases the computation time, decreasing its performance and desirability in real-world applications.
Thus, a continuing need exists for a versatile and efficient analogical reasoning system.